Merge pull request rasbt#37 from nipunsadvilkar/patch-1

small typo

Author: rasbt

faq/different.md

@@ -11,5 +11,5 @@ This is not yet just another "this is how scikit-learn works" book. I aim to exp
 
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-Sure, this book will also contain a decent amount of "math & equations," and in my opinion, there is now way around it if we want to get away from the "black box thinking." However, I hope I managed to make it really easy to follow so that it can be read by a person who doesn't have a strong math background. Many parts of this book will provide examples in scikit-learn, in my opinion the most beautiful and practical machine learning library. However, we will also implement certain algorithms, which are not part of scikit-learn yet, by ourselves to boost our learning experience, for example, adaptive linear neurons, sequential feature selection algorithms, and multilayer artificial neural networks with backpropagation.
+Sure, this book will also contain a decent amount of "math & equations," and in my opinion, there is no way around it if we want to get away from the "black box thinking." However, I hope I managed to make it really easy to follow so that it can be read by a person who doesn't have a strong math background. Many parts of this book will provide examples in scikit-learn, in my opinion the most beautiful and practical machine learning library. However, we will also implement certain algorithms, which are not part of scikit-learn yet, by ourselves to boost our learning experience, for example, adaptive linear neurons, sequential feature selection algorithms, and multilayer artificial neural networks with backpropagation.
 However, this book is not only about learning algorithms, but I also have a strong emphasis on "best practices" and everything that comes before and after the "model learning" in a machine learning application pipeline. Dealing with missing data, transforming categorical data into proper formats, extracting features, evaluating models via cross-validation, comparing algorithms with nested cross-validation, plotting receiver operator characteristics just to name a few ... All in all, this (350 page) short book provides you with everything you need to apply machine learning to real-world problem solving!